feat(cli): charger les moteurs optionnels depuis les modeles embarques

eds_pseudo_manager.py

@@ -9,6 +9,7 @@ Mapping des 13 labels EDS-Pseudo vers les clés PLACEHOLDERS du core d'anonymisa
 Dépendance : pip install 'edsnlp[ml]>=0.12.0'
 """
 from __future__ import annotations
+import sys
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
@@ -41,6 +42,26 @@ EDS_MODELS_CATALOG: Dict[str, str] = {
     "EDS-Pseudo AP-HP (edsnlp)": "AP-HP/eds-pseudo-public",
 }
 
+DEFAULT_MODEL = "AP-HP/eds-pseudo-public"
+BUNDLED_MODEL_DIR = "eds-pseudo-public"
+
+
+def _app_dir() -> Path:
+    if getattr(sys, "frozen", False):
+        return Path(getattr(sys, "_MEIPASS", Path(sys.executable).parent))
+    return Path(__file__).resolve().parent
+
+
+def _bundled_model_path(cache_dir: Optional[Path] = None) -> Optional[Path]:
+    candidates = []
+    if cache_dir is not None:
+        candidates.append(Path(cache_dir) / BUNDLED_MODEL_DIR)
+    candidates.append(_app_dir() / "models" / BUNDLED_MODEL_DIR)
+    for candidate in candidates:
+        if candidate.is_dir():
+            return candidate
+    return None
+
 
 class EdsPseudoManager:
     """Gestionnaire pour le modèle EDS-Pseudo (edsnlp). Même interface que NerModelManager."""
@@ -54,16 +75,21 @@ class EdsPseudoManager:
     def is_loaded(self) -> bool:
         return self._loaded and self._nlp is not None
 
-    def load(self, model_id_or_path: str = "AP-HP/eds-pseudo-public") -> None:
+    def load(self, model_id_or_path: str = DEFAULT_MODEL) -> None:
         if not _EDSNLP_AVAILABLE:
             raise RuntimeError("edsnlp non disponible. Installez : pip install 'edsnlp[ml]>=0.12.0'")
         self.unload()
-        self.model_id = model_id_or_path
-        path = Path(model_id_or_path)
+        source = model_id_or_path
+        if model_id_or_path == DEFAULT_MODEL:
+            bundled = _bundled_model_path(self.cache_dir)
+            if bundled is not None:
+                source = str(bundled)
+        self.model_id = source
+        path = Path(source)
         if path.is_dir():
             self._nlp = edsnlp.load(path)
         else:
-            self._nlp = edsnlp.load(model_id_or_path)
+            self._nlp = edsnlp.load(source)
         # Activer les scores de confiance NER (edsnlp >= 0.16)
         try:
             ner_pipe = self._nlp.get_pipe('ner')